MGOF: Stata module to perform goodness-of-fit tests for multinomial data

mgof computes goodness-of-fit tests for the distribution of a discrete (categorical, multinomial) variable. The default is to perform classical large sample chi-squared approximation tests based on Pearson's X2 statistic and the log likelihood ratio (G2) statistic or a statistic from the Cressie-Read family. Alternatively, mgof computes exact tests using Monte Carlo methods or exhaustive enumeration. A Kolmogorov-Smirnov test for discrete data is also provided. The moremata package, also available from SSC, is required.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

A new Stata command called -mgof- is introduced. The command is used to compute distributional tests for discrete (categorical, multinomial) variables. Apart from classic large sample $\chi^2$-approximation tests based on Pearson's $X^2$, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read 1984), large sample tests for complex survey designs and exact tests for small samples are supported. The complex survey correction is based on the approach by Rao and Scott (1981) and parallels the survey design correction used for independence tests in -svy:tabulate-. The exact tests are computed using Monte Carlo methods or exhaustive enumeration. An exact Kolmogorov-Smirnov test for discrete data is also provided.

Value Creation in Family Firms: A Model of Fit

We propose a framework describing how family ownership can create or destroy value depending on the goals, resources, and governance of the family firm, which are each influenced by the family owners. Taking a contingency perspective, we suggest that a fit is required for all three elements – family- influenced goals, resources, and governance – for the family firm to flourish over generations. We conclude with a suggested research agenda indicating research opportunities at the nexus of these identified elements. Further we provide some guiding questions for practitioners that might stimulate fruitful discussions among family firm owners and managers about how to realize ‘‘fit.’’

Quantification of retinal layer thickness changes in acute macular neuroretinopathy.

PURPOSE To quantitatively evaluate retinal layer thickness changes in acute macular neuroretinopathy (AMN). METHODS AMN areas were identified using near-infrared reflectance (NIR) images. Intraretinal layer segmentation using Heidelberg software was performed. The inbuilt ETDRS -grid was moved onto the AMN lesion and the mean retinal layer thicknesses of the central grid were recorded and compared with the corresponding area of the fellow eye at initial presentation and during follow-up. RESULTS Eleven patients were included (mean age 26±6 years). AMN lesions at baseline had a significantly thinner outer nuclear layer (ONL) (51±21 µm vs 73±17 µm, p=0.002). The other layers, including inner nuclear layer (37±8 µm vs 38±6 µm, p=0.9) and outer plexiform layer (OPL) (45±19 µm vs 33±16 µm, p=0.1) did not show significant differences between the study eyes and fellow eyes. Adjacent to NIR image lesions, areas of OPL thickening were identified (study eye: 50±14 µm vs fellow eye: 39±16 µm, p=0.005) with corresponding thinning of ONL (study eye: 52±16 µm vs fellow eye: 69±16 µm, p=0.002). CONCLUSIONS AMN presents with characteristic quantitative retinal changes and the extent of the lesion may be more extensive than initially presumed from NIR image lesions.